Reconstructing probability distributions with Gaussian processes
Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). Here, the reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new chain with 40 times as many points in only 30 min. Our likelihood reconstruction tool is made publicly available online.
- Research Organization:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- SC0012704
- OSTI ID:
- 1573465
- Report Number(s):
- BNL-212314-2019-JAAM; TRN: US2001369
- Journal Information:
- Monthly Notices of the Royal Astronomical Society, Vol. 489, Issue 3; ISSN 0035-8711
- Publisher:
- Royal Astronomical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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